Overview

Dataset statistics

Number of variables15
Number of observations2000
Missing cells1892
Missing cells (%)6.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory234.5 KiB
Average record size in memory120.1 B

Variable types

Numeric8
Categorical7

Alerts

Genetic_Pedigree_Coefficient is highly overall correlated with Blood_Pressure_AbnormalityHigh correlation
Blood_Pressure_Abnormality is highly overall correlated with Genetic_Pedigree_CoefficientHigh correlation
Sex is highly overall correlated with PregnancyHigh correlation
Pregnancy is highly overall correlated with SexHigh correlation
Genetic_Pedigree_Coefficient has 92 (4.6%) missing valuesMissing
Pregnancy has 1558 (77.9%) missing valuesMissing
alcohol_consumption_per_day has 242 (12.1%) missing valuesMissing
Patient_Number is uniformly distributedUniform
Patient_Number has unique valuesUnique

Reproduction

Analysis started2023-09-19 23:34:43.924218
Analysis finished2023-09-19 23:35:18.217755
Duration34.29 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

Patient_Number
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct2000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1000.5
Minimum1
Maximum2000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2023-09-19T23:35:18.419291image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile100.95
Q1500.75
median1000.5
Q31500.25
95-th percentile1900.05
Maximum2000
Range1999
Interquartile range (IQR)999.5

Descriptive statistics

Standard deviation577.49459
Coefficient of variation (CV)0.57720599
Kurtosis-1.2
Mean1000.5
Median Absolute Deviation (MAD)500
Skewness0
Sum2001000
Variance333500
MonotonicityStrictly increasing
2023-09-19T23:35:18.732323image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.1%
1330 1
 
0.1%
1343 1
 
0.1%
1342 1
 
0.1%
1341 1
 
0.1%
1340 1
 
0.1%
1339 1
 
0.1%
1338 1
 
0.1%
1337 1
 
0.1%
1336 1
 
0.1%
Other values (1990) 1990
99.5%
ValueCountFrequency (%)
1 1
0.1%
2 1
0.1%
3 1
0.1%
4 1
0.1%
5 1
0.1%
6 1
0.1%
7 1
0.1%
8 1
0.1%
9 1
0.1%
10 1
0.1%
ValueCountFrequency (%)
2000 1
0.1%
1999 1
0.1%
1998 1
0.1%
1997 1
0.1%
1996 1
0.1%
1995 1
0.1%
1994 1
0.1%
1993 1
0.1%
1992 1
0.1%
1991 1
0.1%

Blood_Pressure_Abnormality
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.8 KiB
0
1013 
1
987 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 1013
50.6%
1 987
49.4%

Length

2023-09-19T23:35:19.065443image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-19T23:35:19.327632image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 1013
50.6%
1 987
49.4%

Most occurring characters

ValueCountFrequency (%)
0 1013
50.6%
1 987
49.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1013
50.6%
1 987
49.4%

Most occurring scripts

ValueCountFrequency (%)
Common 2000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1013
50.6%
1 987
49.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1013
50.6%
1 987
49.4%

Level_of_Hemoglobin
Real number (ℝ)

Distinct757
Distinct (%)37.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.710035
Minimum8.1
Maximum17.56
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2023-09-19T23:35:19.602443image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum8.1
5-th percentile8.58
Q110.1475
median11.33
Q312.945
95-th percentile16.01
Maximum17.56
Range9.46
Interquartile range (IQR)2.7975

Descriptive statistics

Standard deviation2.1867006
Coefficient of variation (CV)0.18673733
Kurtosis-0.18428798
Mean11.710035
Median Absolute Deviation (MAD)1.36
Skewness0.65706609
Sum23420.07
Variance4.7816597
MonotonicityNot monotonic
2023-09-19T23:35:19.913491image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12.07 11
 
0.5%
11.58 10
 
0.5%
11.54 9
 
0.4%
11.95 8
 
0.4%
11.19 8
 
0.4%
10.55 8
 
0.4%
10.38 8
 
0.4%
11.16 8
 
0.4%
10.89 8
 
0.4%
10.98 8
 
0.4%
Other values (747) 1914
95.7%
ValueCountFrequency (%)
8.1 2
0.1%
8.12 1
 
0.1%
8.13 4
0.2%
8.15 2
0.1%
8.16 2
0.1%
8.17 4
0.2%
8.18 3
0.1%
8.19 1
 
0.1%
8.2 2
0.1%
8.21 1
 
0.1%
ValueCountFrequency (%)
17.56 1
 
0.1%
17.54 1
 
0.1%
17.53 1
 
0.1%
17.52 2
0.1%
17.51 1
 
0.1%
17.48 1
 
0.1%
17.45 1
 
0.1%
17.44 3
0.1%
17.39 1
 
0.1%
17.35 1
 
0.1%

Genetic_Pedigree_Coefficient
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct101
Distinct (%)5.3%
Missing92
Missing (%)4.6%
Infinite0
Infinite (%)0.0%
Mean0.49481656
Minimum0
Maximum1
Zeros17
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2023-09-19T23:35:20.224732image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.04
Q10.24
median0.49
Q30.74
95-th percentile0.9565
Maximum1
Range1
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.29173588
Coefficient of variation (CV)0.58958391
Kurtosis-1.1785628
Mean0.49481656
Median Absolute Deviation (MAD)0.25
Skewness0.015177458
Sum944.11
Variance0.085109825
MonotonicityNot monotonic
2023-09-19T23:35:20.534492image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.86 32
 
1.6%
0.13 30
 
1.5%
0.63 28
 
1.4%
0.56 27
 
1.4%
0.17 27
 
1.4%
0.99 26
 
1.3%
0.95 25
 
1.2%
0.06 25
 
1.2%
0.25 25
 
1.2%
0.46 25
 
1.2%
Other values (91) 1638
81.9%
(Missing) 92
 
4.6%
ValueCountFrequency (%)
0 17
0.9%
0.01 23
1.1%
0.02 24
1.2%
0.03 17
0.9%
0.04 23
1.1%
0.05 15
0.8%
0.06 25
1.2%
0.07 11
0.5%
0.08 21
1.1%
0.09 21
1.1%
ValueCountFrequency (%)
1 18
0.9%
0.99 26
1.3%
0.98 19
0.9%
0.97 18
0.9%
0.96 15
0.8%
0.95 25
1.2%
0.94 18
0.9%
0.93 13
0.7%
0.92 21
1.1%
0.91 11
0.5%

Age
Real number (ℝ)

Distinct58
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.5585
Minimum18
Maximum75
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2023-09-19T23:35:20.830431image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile20
Q132
median46
Q362
95-th percentile73
Maximum75
Range57
Interquartile range (IQR)30

Descriptive statistics

Standard deviation17.107832
Coefficient of variation (CV)0.36744809
Kurtosis-1.2482315
Mean46.5585
Median Absolute Deviation (MAD)15
Skewness0.02117832
Sum93117
Variance292.67792
MonotonicityNot monotonic
2023-09-19T23:35:21.202525image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18 46
 
2.3%
72 45
 
2.2%
71 43
 
2.1%
21 43
 
2.1%
69 41
 
2.1%
25 41
 
2.1%
53 41
 
2.1%
29 40
 
2.0%
49 40
 
2.0%
39 40
 
2.0%
Other values (48) 1580
79.0%
ValueCountFrequency (%)
18 46
2.3%
19 27
1.4%
20 29
1.5%
21 43
2.1%
22 34
1.7%
23 27
1.4%
24 35
1.8%
25 41
2.1%
26 34
1.7%
27 37
1.8%
ValueCountFrequency (%)
75 36
1.8%
74 39
1.9%
73 37
1.8%
72 45
2.2%
71 43
2.1%
70 32
1.6%
69 41
2.1%
68 38
1.9%
67 32
1.6%
66 34
1.7%

BMI
Real number (ℝ)

Distinct41
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.0815
Minimum10
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2023-09-19T23:35:21.714267image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile11
Q120
median30
Q340
95-th percentile48
Maximum50
Range40
Interquartile range (IQR)20

Descriptive statistics

Standard deviation11.761208
Coefficient of variation (CV)0.39097812
Kurtosis-1.1826209
Mean30.0815
Median Absolute Deviation (MAD)10
Skewness-0.017555474
Sum60163
Variance138.32602
MonotonicityNot monotonic
2023-09-19T23:35:22.240195image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
11 62
 
3.1%
38 62
 
3.1%
34 59
 
2.9%
26 59
 
2.9%
41 57
 
2.9%
21 57
 
2.9%
20 54
 
2.7%
35 53
 
2.6%
40 53
 
2.6%
29 52
 
2.6%
Other values (31) 1432
71.6%
ValueCountFrequency (%)
10 48
2.4%
11 62
3.1%
12 42
2.1%
13 38
1.9%
14 44
2.2%
15 52
2.6%
16 47
2.4%
17 39
1.9%
18 49
2.5%
19 45
2.2%
ValueCountFrequency (%)
50 50
2.5%
49 46
2.3%
48 45
2.2%
47 49
2.5%
46 51
2.5%
45 45
2.2%
44 41
2.1%
43 52
2.6%
42 50
2.5%
41 57
2.9%

Sex
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.8 KiB
0
1008 
1
992 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1008
50.4%
1 992
49.6%

Length

2023-09-19T23:35:23.595111image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-19T23:35:24.022607image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 1008
50.4%
1 992
49.6%

Most occurring characters

ValueCountFrequency (%)
0 1008
50.4%
1 992
49.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1008
50.4%
1 992
49.6%

Most occurring scripts

ValueCountFrequency (%)
Common 2000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1008
50.4%
1 992
49.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1008
50.4%
1 992
49.6%

Pregnancy
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)0.5%
Missing1558
Missing (%)77.9%
Memory size15.8 KiB
0.0
243 
1.0
199 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1326
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 243
 
12.2%
1.0 199
 
10.0%
(Missing) 1558
77.9%

Length

2023-09-19T23:35:24.485285image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-19T23:35:24.809573image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 243
55.0%
1.0 199
45.0%

Most occurring characters

ValueCountFrequency (%)
0 685
51.7%
. 442
33.3%
1 199
 
15.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 884
66.7%
Other Punctuation 442
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 685
77.5%
1 199
 
22.5%
Other Punctuation
ValueCountFrequency (%)
. 442
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1326
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 685
51.7%
. 442
33.3%
1 199
 
15.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1326
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 685
51.7%
. 442
33.3%
1 199
 
15.0%

Smoking
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.8 KiB
1
1019 
0
981 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1 1019
50.9%
0 981
49.0%

Length

2023-09-19T23:35:25.035201image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-19T23:35:25.301639image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 1019
50.9%
0 981
49.0%

Most occurring characters

ValueCountFrequency (%)
1 1019
50.9%
0 981
49.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1019
50.9%
0 981
49.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1019
50.9%
0 981
49.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1019
50.9%
0 981
49.0%

Physical_activity
Real number (ℝ)

Distinct1951
Distinct (%)97.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25254.425
Minimum628
Maximum49980
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2023-09-19T23:35:25.562598image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum628
5-th percentile3141.75
Q113605.75
median25353
Q337382.25
95-th percentile47170.2
Maximum49980
Range49352
Interquartile range (IQR)23776.5

Descriptive statistics

Standard deviation14015.44
Coefficient of variation (CV)0.55496967
Kurtosis-1.1617269
Mean25254.425
Median Absolute Deviation (MAD)11893.5
Skewness-0.010559367
Sum50508849
Variance1.9643255 × 108
MonotonicityNot monotonic
2023-09-19T23:35:25.883813image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29513 2
 
0.1%
11432 2
 
0.1%
26274 2
 
0.1%
13144 2
 
0.1%
36769 2
 
0.1%
38769 2
 
0.1%
37260 2
 
0.1%
40772 2
 
0.1%
40158 2
 
0.1%
41415 2
 
0.1%
Other values (1941) 1980
99.0%
ValueCountFrequency (%)
628 1
0.1%
745 1
0.1%
768 2
0.1%
774 1
0.1%
784 1
0.1%
791 1
0.1%
799 1
0.1%
814 1
0.1%
829 1
0.1%
847 1
0.1%
ValueCountFrequency (%)
49980 1
0.1%
49940 1
0.1%
49926 1
0.1%
49915 1
0.1%
49806 1
0.1%
49783 1
0.1%
49759 1
0.1%
49682 1
0.1%
49671 1
0.1%
49665 1
0.1%

salt_content_in_the_diet
Real number (ℝ)

Distinct1945
Distinct (%)97.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24926.097
Minimum22
Maximum49976
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2023-09-19T23:35:26.177820image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum22
5-th percentile2462.1
Q113151.75
median25046.5
Q336839.75
95-th percentile47202.25
Maximum49976
Range49954
Interquartile range (IQR)23688

Descriptive statistics

Standard deviation14211.693
Coefficient of variation (CV)0.57015314
Kurtosis-1.1549638
Mean24926.097
Median Absolute Deviation (MAD)11813
Skewness-0.021797848
Sum49852194
Variance2.0197221 × 108
MonotonicityNot monotonic
2023-09-19T23:35:26.493004image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24961 2
 
0.1%
23332 2
 
0.1%
46834 2
 
0.1%
1769 2
 
0.1%
28517 2
 
0.1%
28465 2
 
0.1%
14561 2
 
0.1%
16005 2
 
0.1%
26078 2
 
0.1%
23468 2
 
0.1%
Other values (1935) 1980
99.0%
ValueCountFrequency (%)
22 1
0.1%
44 1
0.1%
58 1
0.1%
62 1
0.1%
66 1
0.1%
105 1
0.1%
144 1
0.1%
150 1
0.1%
154 1
0.1%
161 1
0.1%
ValueCountFrequency (%)
49976 1
0.1%
49956 1
0.1%
49846 1
0.1%
49800 1
0.1%
49778 1
0.1%
49710 1
0.1%
49700 1
0.1%
49644 1
0.1%
49642 1
0.1%
49626 1
0.1%

alcohol_consumption_per_day
Real number (ℝ)

Distinct488
Distinct (%)27.8%
Missing242
Missing (%)12.1%
Infinite0
Infinite (%)0.0%
Mean251.00853
Minimum0
Maximum499
Zeros9
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2023-09-19T23:35:26.791837image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile28.85
Q1126.25
median250
Q3377.75
95-th percentile473.15
Maximum499
Range499
Interquartile range (IQR)251.5

Descriptive statistics

Standard deviation143.65188
Coefficient of variation (CV)0.57229881
Kurtosis-1.2176786
Mean251.00853
Median Absolute Deviation (MAD)126
Skewness-0.0082591289
Sum441273
Variance20635.864
MonotonicityNot monotonic
2023-09-19T23:35:27.102647image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
253 11
 
0.5%
144 10
 
0.5%
401 10
 
0.5%
302 10
 
0.5%
347 9
 
0.4%
0 9
 
0.4%
485 9
 
0.4%
206 8
 
0.4%
81 8
 
0.4%
180 8
 
0.4%
Other values (478) 1666
83.3%
(Missing) 242
 
12.1%
ValueCountFrequency (%)
0 9
0.4%
1 3
 
0.1%
2 3
 
0.1%
3 5
0.2%
4 2
 
0.1%
5 4
0.2%
6 5
0.2%
8 4
0.2%
9 3
 
0.1%
11 4
0.2%
ValueCountFrequency (%)
499 2
 
0.1%
497 1
 
0.1%
496 3
0.1%
495 5
0.2%
494 4
0.2%
493 1
 
0.1%
492 3
0.1%
491 4
0.2%
490 3
0.1%
488 4
0.2%

Level_of_Stress
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.8 KiB
3
691 
1
666 
2
643 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2000
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row3
3rd row2
4th row1
5th row2

Common Values

ValueCountFrequency (%)
3 691
34.5%
1 666
33.3%
2 643
32.1%

Length

2023-09-19T23:35:27.383362image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-19T23:35:27.658189image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
3 691
34.5%
1 666
33.3%
2 643
32.1%

Most occurring characters

ValueCountFrequency (%)
3 691
34.5%
1 666
33.3%
2 643
32.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 691
34.5%
1 666
33.3%
2 643
32.1%

Most occurring scripts

ValueCountFrequency (%)
Common 2000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 691
34.5%
1 666
33.3%
2 643
32.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 691
34.5%
1 666
33.3%
2 643
32.1%

Chronic_kidney_disease
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.8 KiB
1
1010 
0
990 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 1010
50.5%
0 990
49.5%

Length

2023-09-19T23:35:27.893777image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-19T23:35:28.135301image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 1010
50.5%
0 990
49.5%

Most occurring characters

ValueCountFrequency (%)
1 1010
50.5%
0 990
49.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1010
50.5%
0 990
49.5%

Most occurring scripts

ValueCountFrequency (%)
Common 2000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1010
50.5%
0 990
49.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1010
50.5%
0 990
49.5%

Adrenal_and_thyroid_disorders
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.8 KiB
0
1113 
1
887 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1113
55.6%
1 887
44.4%

Length

2023-09-19T23:35:28.673214image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-19T23:35:28.917269image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 1113
55.6%
1 887
44.4%

Most occurring characters

ValueCountFrequency (%)
0 1113
55.6%
1 887
44.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1113
55.6%
1 887
44.4%

Most occurring scripts

ValueCountFrequency (%)
Common 2000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1113
55.6%
1 887
44.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1113
55.6%
1 887
44.4%

Interactions

2023-09-19T23:35:14.634015image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T23:34:51.034684image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T23:34:56.476292image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T23:35:01.141461image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T23:35:04.541838image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T23:35:06.955735image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T23:35:10.226246image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T23:35:12.517858image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T23:35:14.915679image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T23:34:51.538831image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T23:34:57.356531image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T23:35:01.533821image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T23:35:04.910091image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T23:35:07.293405image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T23:35:10.588405image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T23:35:12.795142image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T23:35:15.146915image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T23:34:51.920706image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T23:34:57.961936image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T23:35:01.967196image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T23:35:05.184352image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T23:35:07.731159image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T23:35:10.975904image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T23:35:13.049069image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T23:35:15.420945image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T23:34:52.647464image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T23:34:58.472338image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T23:35:02.430596image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T23:35:05.433100image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T23:35:08.150335image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T23:35:11.236483image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T23:35:13.320276image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T23:35:15.680439image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T23:34:53.127264image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T23:34:58.982644image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T23:35:02.818305image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T23:35:05.693449image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T23:35:08.546371image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T23:35:11.493770image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T23:35:13.584407image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T23:35:15.944433image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T23:34:54.017505image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T23:34:59.629893image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T23:35:03.285439image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T23:35:05.978010image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T23:35:08.995245image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T23:35:11.764394image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T23:35:13.870806image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T23:35:16.181771image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T23:34:54.811404image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T23:35:00.108569image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T23:35:03.733413image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T23:35:06.424359image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T23:35:09.406791image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T23:35:12.031950image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T23:35:14.143938image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T23:35:16.442591image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T23:34:55.723063image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T23:35:00.611866image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T23:35:04.126722image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T23:35:06.693768image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T23:35:09.813517image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T23:35:12.278136image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T23:35:14.389546image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-09-19T23:35:29.128760image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Patient_NumberLevel_of_HemoglobinGenetic_Pedigree_CoefficientAgeBMIPhysical_activitysalt_content_in_the_dietalcohol_consumption_per_dayBlood_Pressure_AbnormalitySexPregnancySmokingLevel_of_StressChronic_kidney_diseaseAdrenal_and_thyroid_disorders
Patient_Number1.0000.005-0.005-0.029-0.0000.0170.024-0.0260.0350.0590.0000.0000.0000.0580.000
Level_of_Hemoglobin0.0051.000-0.023-0.1590.102-0.0370.0170.0000.4940.4760.4340.0370.0280.2150.191
Genetic_Pedigree_Coefficient-0.005-0.0231.000-0.023-0.010-0.0080.039-0.0200.5130.0190.0840.0000.0000.2290.148
Age-0.029-0.159-0.0231.0000.0290.026-0.0460.0170.0790.0680.0190.0000.0290.0410.000
BMI-0.0000.102-0.0100.0291.000-0.0050.026-0.0390.0570.0000.0000.0000.0520.0000.024
Physical_activity0.017-0.037-0.0080.026-0.0051.000-0.033-0.0120.0380.0000.0370.0000.0000.0000.000
salt_content_in_the_diet0.0240.0170.039-0.0460.026-0.0331.000-0.0300.0380.0820.0000.0000.0000.0000.044
alcohol_consumption_per_day-0.0260.000-0.0200.017-0.039-0.012-0.0301.0000.0250.0000.0000.0400.0000.0000.048
Blood_Pressure_Abnormality0.0350.4940.5130.0790.0570.0380.0380.0251.0000.0490.0000.0000.0000.4280.317
Sex0.0590.4760.0190.0680.0000.0000.0820.0000.0491.0001.0000.0000.0000.0110.000
Pregnancy0.0000.4340.0840.0190.0000.0370.0000.0000.0001.0001.0000.1510.0600.0000.021
Smoking0.0000.0370.0000.0000.0000.0000.0000.0400.0000.0000.1511.0000.0180.0090.000
Level_of_Stress0.0000.0280.0000.0290.0520.0000.0000.0000.0000.0000.0600.0181.0000.0360.000
Chronic_kidney_disease0.0580.2150.2290.0410.0000.0000.0000.0000.4280.0110.0000.0090.0361.0000.116
Adrenal_and_thyroid_disorders0.0000.1910.1480.0000.0240.0000.0440.0480.3170.0000.0210.0000.0000.1161.000

Missing values

2023-09-19T23:35:17.101235image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-09-19T23:35:17.660161image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-09-19T23:35:18.061910image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Patient_NumberBlood_Pressure_AbnormalityLevel_of_HemoglobinGenetic_Pedigree_CoefficientAgeBMISexPregnancySmokingPhysical_activitysalt_content_in_the_dietalcohol_consumption_per_dayLevel_of_StressChronic_kidney_diseaseAdrenal_and_thyroid_disorders
01111.280.90342311.004596148071NaN211
1209.750.2354331NaN02610625333205.0300
23110.790.9170490NaN099952946567.0210
34011.000.4371500NaN0106357439242.0110
45114.170.8352190NaN01561949644397.0200
56011.640.5423480NaN1270427513NaN300
67111.690.75434111.003836932967206.0311
78012.700.4148200NaN02978126749134.0200
89010.880.6872440NaN0814960799.0300
910114.560.6140440NaN012781271595.0200
Patient_NumberBlood_Pressure_AbnormalityLevel_of_HemoglobinGenetic_Pedigree_CoefficientAgeBMISexPregnancySmokingPhysical_activitysalt_content_in_the_dietalcohol_consumption_per_dayLevel_of_StressChronic_kidney_diseaseAdrenal_and_thyroid_disorders
19901991111.210.0163250NaN132903454050.0300
19911992115.530.1222240NaN04832516514NaN211
1992199319.380.4960391NaN14659129557125.0111
1993199409.691.0073421NaN1433443623048.0301
19941995011.070.6658311NaN03860322836379.0200
19951996110.140.0269261NaN12611847568144.0310
19961997111.771.00244511.0125728063NaN311
19971998116.910.2218420NaN01493324753NaN211
19981999011.150.7246451NaN11815715275253.0301
19992000111.360.0941450NaN02072930463230.0110